## **THE ROLE AND THE TEAM** As an **Applied Scientist**, you will contribute to algorithmic research-driven software development in a logistics team. At Zalando, we process millions of customer orders every week and our systems make critical scheduling, planning, and routing decisions within our warehouses. Predictive warehousing allows us to preemptively take actions, which improve the warehouse efficiency by solving a stochastic optimization problem, at the crossroads of combinatorial optimization and data science. Our research pipeline enables fast incremental improvements which together with the scale of our decisions has a direct impact on the productivity of our warehouses. **INCLUSIVE BY DESIGN** At Zalando, our vision is to be inclusive by design. And this vision starts with our hiring - we do not discriminate on the basis of gender identity, sexual orientation, personal expression, ethnicity, religious belief, or disability status. You are welcome to leave out your picture, age, or marital status from your application. We only assess candidates on their qualifications and merit. We want to provide you with a great candidate experience. Feel free to inform us of any accommodations you may need, so we can best support you throughout the hiring process. do.BETTER - our diversity & inclusion strategy: https://corporate.zalando.com/en/our-impact/dobetter-our-diversity-and-inclusion-strategy Our employee resource groups: https://corporate.zalando.com/en/our-impact/our-employee-resource-groups ## **WHAT WE’D LOVE YOU TO DO (AND LOVE DOING)** - Contribute to the research, design, and implementation of algorithms and predictive models. - Deliver with the team end-to-end solutions, which includes research, prototyping, implementing production software, as well as testing and operating the highly available production system. - Influence our research and development methodologies to foster a scalable and data-driven architecture. - Support and coach other Applied Scientists by sharing your knowledge and expertise in applied research. ## ## **WE’D LOVE TO MEET YOU IF** - You hold a **Master’s or PhD in Mathematics, Computer Science**, or another quantitative field, ideally with real-world applications in the context of **operational research and machine learning**. - You demonstrate a practical and theoretical understanding of various algorithmic techniques (e.g., from combinatorics, graph theory, control theory), mathematical modeling (e.g., from operations research, statistics, experimentation), and machine learning approaches (e.g. forecasting, Bayesian networks). - You are passionate about solving interesting mathematical problems and seek more real-world impact. - Experience with software engineering related to microservice architecture or data processing is a plus. - Your strengths extend to your ability to effectively collaborate within an agile and cross-functional team environment, engaging with engineers, product owners, and fellow applied scientists. Want to know what kind of problems you will be working on? You can find on this[google doc](https://docs.google.com/document/d/14K72TC_o6jKs8IdIVbdvtd7akTM9Qor71GCmH9xNxnY/edit?tab=t.0) a stochastic optimization problem that showcases an example. If you like solving mathematical puzzles, send us your solution instead of uploading a cover letter. ## **OUR OFFER** Zalando provides a range of benefits, here’s an overview of what you can expect. Ask your Talent Acquisition Partner to learn more about what we offer. - Employee shares program - 40% off fashion and beauty products sold and shipped by Zalando, 30% off Zalando Lounge, discounts from external partners - 2 paid volunteering days a year - Work from abroad for up to 30 working days a year - 27 days of vacation a year to start - Relocation assistance available (subject to prior agreement) - Family services, including counseling and support - Health and wellbeing options (including Gympass) - Mental health support and coaching available Learn all about Zalando and our values here: https://jobs.zalando.com/en/?gh_src=22377bdd1us